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多色谱流式细胞术动态评估 NSCLC 免疫检查点抑制剂反应的血液免疫细胞。

Dynamic evaluation of blood immune cells predictive of response to immune checkpoint inhibitors in NSCLC by multicolor spectrum flow cytometry.

机构信息

Division of Hematology/Oncology, Department of Internal Medicine, University of California Davis Comprehensive Cancer Center, University of California Davis School of Medicine, Sacramento, CA, United States.

Department of Pathology and Laboratory Medicine, Dartmouth-Hitchcock Medical Center, Geisel School of Medicine, Dartmouth, NH, United States.

出版信息

Front Immunol. 2023 Aug 10;14:1206631. doi: 10.3389/fimmu.2023.1206631. eCollection 2023.

DOI:10.3389/fimmu.2023.1206631
PMID:37638022
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10449448/
Abstract

INTRODUCTION

Immune checkpoint inhibitors (ICIs) only benefit a subset of cancer patients, underlining the need for predictive biomarkers for patient selection. Given the limitations of tumor tissue availability, flow cytometry of peripheral blood mononuclear cells (PBMCs) is considered a noninvasive method for immune monitoring. This study explores the use of spectrum flow cytometry, which allows a more comprehensive analysis of a greater number of markers using fewer immune cells, to identify potential blood immune biomarkers and monitor ICI treatment in non-small-cell lung cancer (NSCLC) patients.

METHODS

PBMCs were collected from 14 non-small-cell lung cancer (NSCLC) patients before and after ICI treatment and 4 healthy human donors. Using spectrum flow cytometry, 24 immune cell markers were simultaneously monitored using only 1 million PBMCs. The results were also compared with those from clinical flow cytometry and bulk RNA sequencing analysis.

RESULTS

Our findings showed that the measurement of CD4+ and CD8+ T cells by spectrum flow cytometry matched well with those by clinical flow cytometry (Pearson R ranging from 0.75 to 0.95) and bulk RNA sequencing analysis (R=0.80, P=1.3 x 10-4). A lower frequency of CD4+ central memory cells before treatment was associated with a longer median progression-free survival (PFS) [Not reached (NR) vs. 5 months; hazard ratio (HR)=8.1, 95% confidence interval (CI) 1.5-42, P=0.01]. A higher frequency of CD4-CD8- double-negative (DN) T cells was associated with a longer PFS (NR vs. 4.45 months; HR=11.1, 95% CI 2.2-55.0, P=0.003). ICIs significantly changed the frequency of cytotoxic CD8+PD1+ T cells, DN T cells, CD16+CD56dim and CD16+CD56- natural killer (NK) cells, and CD14+HLDRhigh and CD11c+HLADR + monocytes. Of these immune cell subtypes, an increase in the frequency of CD16+CD56dim NK cells and CD14+HLADRhigh monocytes after treatment compared to before treatment were associated with a longer PFS (NR vs. 5 months, HR=5.4, 95% CI 1.1-25.7, P=0.03; 7.8 vs. 3.8 months, HR=5.7, 95% CI 169 1.0-31.7, P=0.04), respectively.

CONCLUSION

Our preliminary findings suggest that the use of multicolor spectrum flow cytometry helps identify potential blood immune biomarkers for ICI treatment, which warrants further validation.

摘要

简介

免疫检查点抑制剂(ICIs)仅使一部分癌症患者受益,这突显了需要寻找预测性生物标志物来选择患者。鉴于肿瘤组织可用性的限制,外周血单核细胞(PBMC)的流式细胞术被认为是一种用于免疫监测的非侵入性方法。本研究探讨了使用光谱流式细胞术的可能性,该技术可使用较少的免疫细胞对更多标记物进行更全面的分析,以鉴定潜在的血液免疫生物标志物并监测非小细胞肺癌(NSCLC)患者的 ICI 治疗。

方法

ICI 治疗前后采集了 14 名非小细胞肺癌(NSCLC)患者和 4 名健康供体的 PBMC。使用光谱流式细胞术,仅使用 100 万个 PBMC 同时监测 24 种免疫细胞标记物。结果还与临床流式细胞术和批量 RNA 测序分析进行了比较。

结果

我们的研究结果表明,光谱流式细胞术测量的 CD4+和 CD8+T 细胞与临床流式细胞术(Pearson R 范围为 0.75 至 0.95)和批量 RNA 测序分析(R=0.80,P=1.3×10-4)吻合良好。治疗前 CD4+中央记忆细胞的频率较低与中位无进展生存期(PFS)较长相关[未达到(NR)与 5 个月;危险比(HR)=8.1,95%置信区间(CI)1.5-42,P=0.01]。CD4-CD8-DN 双阴性(DN)T 细胞的频率较高与较长的 PFS 相关(NR 与 4.45 个月;HR=11.1,95%CI 2.2-55.0,P=0.003)。ICI 显著改变了细胞毒性 CD8+PD1+T 细胞、DN T 细胞、CD16+CD56dim 和 CD16+CD56-NK 细胞以及 CD14+HLDRhigh 和 CD11c+HLADR+单核细胞的频率。在这些免疫细胞亚群中,与治疗前相比,治疗后 CD16+CD56dim NK 细胞和 CD14+HLADRhigh 单核细胞的频率增加与更长的 PFS 相关(NR 与 5 个月,HR=5.4,95%CI 1.1-25.7,P=0.03;7.8 与 3.8 个月,HR=5.7,95%CI 169 1.0-31.7,P=0.04)。

结论

我们的初步研究结果表明,使用多色光谱流式细胞术有助于鉴定 ICI 治疗的潜在血液免疫生物标志物,值得进一步验证。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/642b/10449448/e490de06ebfb/fimmu-14-1206631-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/642b/10449448/d01dd612c1c0/fimmu-14-1206631-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/642b/10449448/f48c467e217c/fimmu-14-1206631-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/642b/10449448/6c4f0bf07105/fimmu-14-1206631-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/642b/10449448/b14f88812457/fimmu-14-1206631-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/642b/10449448/ecd5e45dc3de/fimmu-14-1206631-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/642b/10449448/e490de06ebfb/fimmu-14-1206631-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/642b/10449448/d01dd612c1c0/fimmu-14-1206631-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/642b/10449448/f48c467e217c/fimmu-14-1206631-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/642b/10449448/6c4f0bf07105/fimmu-14-1206631-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/642b/10449448/b14f88812457/fimmu-14-1206631-g004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/642b/10449448/e490de06ebfb/fimmu-14-1206631-g006.jpg

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